OpenRaiser/Intern-Atlas huggingface dataset:
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Venue: arXiv cs.AI 2018
Year: 2018

The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors. Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings. We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs. Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
Venue: arXiv cs.CV 2018
Year: 2018

To better detect pedestrians of various scales, deep multi-scale methods usually detect pedestrians of different scales by different in-network layers. However, the semantic levels of features from different layers are usually inconsistent. In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches. As a result, the different branches have the same depth and the output features of different branches have similarly high-level semantics. Due to the difference of receptive fields, the different branches are suitable to detect pedestrians of different scales. Meanwhile, the multi-branch network does not introduce additional parameters by sharing convolutional weights of different branches. To further improve detection performance, skip-layer connections among different branches are used to add context to the branch of relatively small receptive filed, and dilated convolution is incorporated into part branches to enlarge the resolutions of output feature maps. When they are embedded into Faster RCNN architecture, the weighted scores of proposal generation network and proposal classification network are further proposed. Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersons dataset demonstrate the effectiveness of proposed method. On these pedestrian datasets, the proposed method achieves state-of-the-art detection performance. Moreover, experiments on COCO benchmark show the proposed method is also suitable for general object detection.
M-BEHZAD: Minimum distance Based Energy efficiency using Hemisphere Zoning with Advanced Divide-and-Rule Scheme for Wireless Sensor Networks
Venue: arXiv cs.NI 2018
Year: 2018

Routing Protocols are engaged in a vigorous fashion to boost up energy efficiency in WSNs. In this paper, we propose a novel routing protocol; Minimum distance Based Energy efficiency using Hemisphere Zoning with Advanced Divide-and-Rule scheme (M-BEHZAD), to maximize network lifespan, throughput and stability period of the sensors deployed in an un-attended network zone. To accomplish these objectives, static clustering technique along with threshold conscious transmissions have been used. The robustness of our proposed scheme lies in its Cluster Heads (CHs) selection and network field division which we are introducing as 'Hemisphere Zoning (HZ)'. We have implemented 3-Tier architecture to minimize the communication distance which not only leads to a better network performance but also significantly reduces the energy and coverage holes, and results in a longer stability period. We have also utilized Uniform Random Model (URM) to compute packets dropped to make our scheme a more practical approach. Results from comprehensive simulations using MATLAB validate its applicability.
Intra-Frame Error Concealment Scheme using 3D Reversible Data Hiding in Mobile Cloud Environment
Venue: arXiv cs.MM 2018
Year: 2018

Data in mobile cloud environment are mainly transmitted via wireless noisy channels, which may result in transmission errors with a high probability due to its unreliable connectivity. For video transmission, unreliable connectivity may cause significant degradation of the content. Improving or keeping video quality over lossy channel is therefore a very important research topic. Error concealment with data hiding (ECDH) is an effective way to conceal the errors introduced by channels. It can reduce error propagation between neighbor blocks/frames comparing with the methods exploiting temporal/spatial correlations. The existing video ECDH methods often embed the motion vectors (MVs) into the specific locations. Nevertheless, specific embedding locations cannot resist against random errors. To compensate the unreliable connectivity in mobile cloud environment, in this paper, we present a video ECDH scheme using 3D reversible data hiding (RDH), in which each MV is repeated multiple times, and the repeated MVs are embedded into different macroblocks (MBs) randomly. Though the multiple embedding requires more embedding space, satisfactory trade-off between the introduced distortion and the reconstructed video quality can be achieved by tuning the repeating times of the MVs. For random embedding, the lost probability of the MVs decreases rapidly, resulting in better error concealment performance. Experimental results show that the PSNR values gain about 5dB at least comparing with the existing ECDH methods. Meanwhile, the proposed method improves the video quality significantly.
The Transactional Conflict Problem
Venue: arXiv cs.DC 2018
Year: 2018

The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts is to immediately abort one of the transactions, some practical systems consider the alternative of delaying conflict resolution for a short interval, which may allow one of the transactions to commit. The challenge in the transactional conflict problem is to choose the optimal length of this delay interval so as to minimize the overall running time penalty for the conflicting transactions. In this paper, we propose a family of optimal online algorithms for the transactional conflict problem. Specifically, we consider variants of this problem which arise in different implementations of transactional systems, namely "requestor wins" and "requestor aborts" implementations: in the former, the recipient of a coherence request is aborted, whereas in the latter, it is the requestor which has to abort. Both strategies are implemented by real systems. We show that the requestor aborts case can be reduced to a classic instance of the ski rental problem, while the requestor wins case leads to a new version of this classical problem, for which we derive optimal deterministic and randomized algorithms. Moreover, we prove that, under a simplified adversarial model, our algorithms are constant-competitive with the offline optimum in terms of throughput. We validate our algorithmic results empirically through a hardware simulation of hardware transactional memory (HTM), showing that our algorithms can lead to non-trivial performance improvements for classic concurrent data structures.
360° Stance Detection
Venue: arXiv cs.CL 2018
Year: 2018

The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360° Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposition, enabling the user to base their opinion on multiple pieces of diverse evidence.
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
Venue: arXiv cs.CL 2018
Year: 2018

We present an approach where two different models (Deep and Shallow) are trained separately on the data and a weighted average of the outputs is taken as the final result. For the Deep approach, we use different combinations of models like Convolution Neural Network, pretrained word2vec embeddings and LSTMs to get representations which are then used to train a Deep Neural Network. For Clarity prediction, we also use an Attentive Pooling approach for the pooling operation so as to be aware of the Title-Category pair. For the shallow approach, we use boosting technique LightGBM on features generated using title and categories. We find that an ensemble of these approaches does a better job than using them alone suggesting that the results of the deep and shallow approach are highly complementary
Face Alignment in Full Pose Range: A 3D Total Solution
Venue: arXiv cs.CV 2018
Year: 2018

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in the computer vision community. However, most algorithms are designed for faces in small to medium poses (yaw angle is smaller than 45 degrees), which lack the ability to align faces in large poses up to 90 degrees. The challenges are three-fold. Firstly, the commonly used landmark face model assumes that all the landmarks are visible and is therefore not suitable for large poses. Secondly, the face appearance varies more drastically across large poses, from the frontal view to the profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks. We also utilize 3D information to synthesize face images in profile views to provide abundant samples for training. Experiments on the challenging AFLW database show that the proposed approach achieves significant improvements over the state-of-the-art methods.
Multichannel Design of Non uniform Constellations for Broadcast/Multicast Services
Venue: arXiv cs.IT 2018
Year: 2018

Recent studies have shown the potential performance gain of Non Uniform Constellations (NUC) compared to the conventional uniform constellations. NUC can be a promising candidate in 5G systems to increase the data throughput. In the literature, NUC is designed for a specific SNR value and propagation channel. However, in broadcast/multicast services, the received signal by different users will see independent and different channels. Hence, in this paper, we focus on the potential gain of NUC when jointly optimized for more than one propagation channel. In order to assess the gain, we propose an iterative algorithm to jointly optimize the NUC for different channel conditions. The resulting constellations are then compared to uniform constellation and single channel NUC. The simulation results show that the newly designed constellations outperform the classical single channel NUC across different channel conditions when the average performance (across different channels) is considered.
Operator Scaling via Geodesically Convex Optimization, Invariant Theory and Polynomial Identity Testing
Venue: arXiv cs.DS 2018
Year: 2018

We propose a new second-order method for geodesically convex optimization on the natural hyperbolic metric over positive definite matrices. We apply it to solve the operator scaling problem in time polynomial in the input size and logarithmic in the error. This is an exponential improvement over previous algorithms which were analyzed in the usual Euclidean, "commutative" metric (for which the above problem is not convex). Our method is general and applicable to other settings. As a consequence, we solve the equivalence problem for the left-right group action underlying the operator scaling problem. This yields a deterministic polynomial-time algorithm for a new class of Polynomial Identity Testing (PIT) problems, which was the original motivation for studying operator scaling.
Probing Physics Knowledge Using Tools from Developmental Psychology
Venue: arXiv cs.AI 2018
Year: 2018

In order to build agents with a rich understanding of their environment, one key objective is to endow them with a grasp of intuitive physics; an ability to reason about three-dimensional objects, their dynamic interactions, and responses to forces. While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data. In the latter case, one challenge that arises is evaluating the learning system. Research on intuitive physics knowledge in children has long employed a violation of expectations (VOE) method to assess children's mastery of specific physical concepts. We take the novel step of applying this method to artificial learning systems. In addition to introducing the VOE technique, we describe a set of probe datasets inspired by classic test stimuli from developmental psychology. We test a baseline deep learning system on this battery, as well as on a physics learning dataset ("IntPhys") recently posed by another research group. Our results show how the VOE technique may provide a useful tool for tracking physics knowledge in future research.
A Modified Image Comparison Algorithm Using Histogram Features
Venue: arXiv cs.CV 2018
Year: 2018

This article discuss the problem of color image content comparison. Particularly, methods of image content comparison are analyzed, restrictions of color histogram are described and a modified method of images content comparison is proposed. This method uses the color histograms and considers color locations. Testing and analyzing of based and modified algorithms are performed. The modified method shows 97% average precision for a collection containing about 700 images without loss of the advantages of based method, i.e. scale and rotation invariant.
Query Shortest Paths Amidst Growing Discs
Venue: arXiv cs.DS 2018
Year: 2018

The determination of collision-free shortest paths among growing discs has previously been studied for discs with fixed growing rates. Here, we study a more general case of this problem, where: (1) the speeds at which the discs are growing are polynomial functions of degree $\dd$, and (2) the source and destination points are given as query points. We show how to preprocess the $n$ growing discs so that, for two given query points $s$ and $d$, a shortest path from $s$ to $d$ can be found in $O(n^2 \log (\dd n))$ time. The preprocessing time of our algorithm is $O(n^2 \log n + k \log k)$ where $k$ is the number of intersections between the growing discs and the tangent paths (straight line paths which touch the boundaries of two growing discs). We also prove that $k \in O(n^3\dd)$.
Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors
Venue: arXiv cs.SD 2018
Year: 2018

The focus of this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method,we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
Co Hijacking Monitor: Collaborative Detecting and Locating Mechanism for HTTP Spectral Hijacking
Venue: arXiv cs.CR 2018
Year: 2018

With the rapid growth of mobile internet, mobile application, like website navigation, searching, e-Shopping and app download, etc. are all popular in worldwide. Meanwhile, it become more and more popular that traditional HTTP protocol, which is also applying in not only web browsing but also communication between mobile application clients and servers. Besides, it has made HTTP Hijacking profitable. Furthermore, it has brought a lot of troubles for users, network operators and ISP. We analyze the principle of HTTP spectral Hijacking and present a mechanism of collaboratively detecting and locating called Co HijackingMonitor. Experimental result shows that, Co HijackingMonitor can solve the hijacking problem effectively.
Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Deep convolutional networks based methods have brought great breakthrough in images classification, which provides an end-to-end solution for handwritten Chinese character recognition(HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computation cost, and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware device with the limit of computation amount. To solve the storage problem, we propose a novel technique called Global Weighted Arverage Pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output layer to complete recognition as early as possible, which reduces average inference time significantly. Experiments were performed on the ICDAR-2013 offline HCCR dataset, and it is found that the proposed approach only needs 6.9ms for classfying a chracter image on average, and achieves the state-of-the-art accuracy of 97.1% while requiring only 3.3MB for storage.
Maintenance of Strongly Connected Component in Shared-memory Graph
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we present an on-line fully dynamic algorithm for maintaining strongly connected component of a directed graph in a shared memory architecture. The edges and vertices are added or deleted concurrently by fixed number of threads. To the best of our knowledge, this is the first work to propose using linearizable concurrent directed graph and is build using both ordered and unordered list-based set. We provide an empirical comparison against sequential and coarse-grained. The results show our algorithm's throughput is increased between 3 to 6x depending on different workload distributions and applications. We believe that there are huge applications in the on-line graph. Finally, we show how the algorithm can be extended to community detection in on-line graph.
A Deterministic Distributed $2$-Approximation for Weighted Vertex Cover in $O(\log n\logΔ/ \log^2\logΔ)$ Rounds
Venue: arXiv cs.DC 2018
Year: 2018

We present a deterministic distributed $2$-approximation algorithm for the Minimum Weight Vertex Cover problem in the CONGEST model whose round complexity is $O(\log n \log Δ/ \log^2 \log Δ)$. This improves over the currently best known deterministic 2-approximation implied by [KVY94]. Our solution generalizes the $(2+ε)$-approximation algorithm of [BCS17], improving the dependency on $ε^{-1}$ from linear to logarithmic. In addition, for every $ε=(\log Δ)^{-c}$, where $c\geq 1$ is a constant, our algorithm computes a $(2+ε)$-approximation in $O(\log Δ/ \log \log Δ)$~rounds (which is asymptotically optimal).
On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective
Venue: arXiv cs.CR 2018
Year: 2018

Bitcoin cryptocurrency system enables users to transact securely and pseudo-anonymously by using an arbitrary number of aliases (Bitcoin addresses). Cybercriminals exploit these characteristics to commit immutable and presumably untraceable monetary fraud, especially via ransomware; a type of malware that encrypts files of the infected system and demands ransom for decryption. In this paper, we present our comprehensive study on all recent ransomware and report the economic impact of such ransomware from the Bitcoin payment perspective. We also present a lightweight framework to identify, collect, and analyze Bitcoin addresses managed by the same user or group of users (cybercriminals, in this case), which includes a novel approach for classifying a payment as ransom. To verify the correctness of our framework, we compared our findings on CryptoLocker ransomware with the results presented in the literature. Our results align with the results found in the previous works except for the final valuation in USD. The reason for this discrepancy is that we used the average Bitcoin price on the day of each ransom payment whereas the authors of the previous studies used the Bitcoin price on the day of their evaluation. Furthermore, for each investigated ransomware, we provide a holistic view of its genesis, development, the process of infection and execution, and characteristic of ransom demands. Finally, we also release our dataset that contains a detailed transaction history of all the Bitcoin addresses we identified for each ransomware.
Optimal Rendezvous ${\mathcal L}$-Algorithms for Asynchronous Mobile Robots with External-Lights
Venue: arXiv cs.DC 2018
Year: 2018

We study the Rendezvous problem for 2 autonomous mobile robots in asynchronous settings with persistent memory called light. It is well known that Rendezvous is impossible in a basic model when robots have no lights, even if the system is semi-synchronous. On the other hand, Rendezvous is possible if robots have lights of various types with a constant number of colors. If robots can observe not only their own lights but also other robots' lights, their lights are called full-light. If robots can only observe the state of other robots' lights, the lights are called external-light. In this paper, we focus on robots with external-lights in asynchronous settings and a particular class of algorithms (called L-algorithms), where an L-algorithm computes a destination based only on the current colors of observable lights. When considering L-algorithms, Rendezvous can be solved by robots with full-lights and 3 colors in general asynchronous settings (called ASYNC) and the number of colors is optimal under these assumptions. In contrast, there exists no L-algorithms in ASYNC with external-lights regardless of the number of colors. In this paper, we consider a fairly large subclass of ASYNC in which Rendezvous can be solved by L-algorithms using external-lights with a finite number of colors, and we show that the algorithms are optimal in the number of colors they use.